Predictive Score Model for Delayed Graft Function Based on Hypothermic Machine Perfusion Variables in Kidney Transplantation.

Chen-guang Ding; Yang LI; Xiao-hui Tian; Xiao-jun HU; Pu-xu Tian; Xiao-ming Ding; He-li Xiang; Jin Zheng; Wu-jun Xue

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Predictive Score Model for Delayed Graft Function Based on Hypothermic Machine Perfusion Variables in Kidney Transplantation.

Author: Chen-Guang DING ^{1
,

2} ; Yang LI ^{1
,

2} ; Xiao-Hui TIAN ^{1
,

2} ; Xiao-Jun HU ^{1
,

2} ; Pu-Xu TIAN ^{1
,

2} ; Xiao-Ming DING ^{1
,

2} ; He-Li XIANG ^{1
,

2} ; Jin ZHENG ^{1
,

2} ; Wu-Jun XUE ^{1
,

2}
Author Information

1. Department of Kidney Transplantation, Nephropathy Hospital, The First Affiliated Hospital of Xi'an Jiaotong University
2. Institute of Organ Transplantation, Xi'an Jiaotong University, Xi'an, Shaanxi 710061, China.
Publication Type:Journal Article
Keywords: Delayed Graft Function; Donation after Cardiac Death; Hypothermic Machine Perfusion; Kidney Transplantation
MeSH: Adult; Delayed Graft Function; Female; Humans; Immunosuppressive Agents; therapeutic use; Kidney Transplantation; adverse effects; methods; Logistic Models; Male; Multivariate Analysis; Odds Ratio; Organ Preservation
From: Chinese Medical Journal 2018;131(22):2651-2657
CountryChina
Language:English
Abstract: Background:Hypothermic machine perfusion (HMP) is being used more often in cardiac death kidney transplantation; however, the significance of assessing organ quality and predicting delayed graft function (DGF) by HMP parameters is still controversial. Therefore, we used a readily available HMP variable to design a scoring model that can identify the highest risk of DGF and provide the guidance and advice for organ allocation and DCD kidney assessment.
Methods:From September 1, 2012 to August 31, 2016, 366 qualified kidneys were randomly assigned to the development and validation cohorts in a 2:1 distribution. The HMP variables of the development cohort served as candidate univariate predictors for DGF. The independent predictors of DGF were identified by multivariate logistic regression analysis with a P < 0.05. According to the odds ratios (ORs) value, each HMP variable was assigned a weighted integer, and the sum of the integers indicated the total risk score for each kidney. The validation cohort was used to verify the accuracy and reliability of the scoring model.
Results:HMP duration (OR = 1.165, 95% confidence interval [CI]: 1.008-1.360, P = 0.043), resistance (OR = 2.190, 95% CI: 1.032-10.20, P < 0.001), and flow rate (OR = 0.931, 95% CI: 0.894-0.967, P = 0.011) were the independent predictors of identified DGF. The HMP predictive score ranged from 0 to 14, and there was a clear increase in the incidence of DGF, from the low predictive score group to the very high predictive score group. We formed four increasingly serious risk categories (scores 0-3, 4-7, 8-11, and 12-14) according to the frequency associated with the different risk scores of DGF. The HMP predictive score indicates good discriminative power with a c-statistic of 0.706 in the validation cohort, and it had significantly better prediction value for DGF compared to both terminal flow (P = 0.012) and resistance (P = 0.006).
Conclusion:The HMP predictive score is a good noninvasive tool for assessing the quality of DCD kidneys, and it is potentially useful for physicians in making optimal decisions about the organs donated.